# Multilingual Dysarthric Speech Assessment Using Universal Phone Recognition and Language-Specific Phonemic Contrast Modeling

arXiv: https://arxiv.org/abs/2601.21205 · HTML: https://mlsanity.com/paper/2601.21205/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 81 references that are in the map.
- First posted on arXiv 29 Jan 2026.

## Builds on (most cited first, 13 of 13)

- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://mlsanity.com/paper/2109.11680/) (2021)
- [ZIPA: A family of efficient models for multilingual phone recognition](https://mlsanity.com/paper/2505.23170/) (2025)
- [YOLO-Stutter: End-to-end Region-Wise Speech Dysfluency Detection](https://mlsanity.com/paper/2408.15297/) (2024)
- [EasyCall corpus: a dysarthric speech dataset](https://mlsanity.com/paper/2104.02542/) (2021)
- [The taste of IPA: Towards open-vocabulary keyword spotting and forced alignment in any language](https://mlsanity.com/paper/2311.08323/) (2023)
- [POWSM: A Phonetic Open Whisper-Style Speech Foundation Model](https://mlsanity.com/paper/2510.24992/) (2025)
- [Speech Intelligibility Assessment of Dysarthric Speech by using Goodness of Pronunciation with Uncertainty Quantification](https://mlsanity.com/paper/2305.18392/) (2023)
- [Cross-lingual Dysarthria Severity Classification for English, Korean, and Tamil](https://mlsanity.com/paper/2209.12942/) (2022)
- [Automatic Severity Classification of Dysarthric speech by using Self-supervised Model with Multi-task Learning](https://mlsanity.com/paper/2210.15387/) (2022)
- [PRiSM: Benchmarking Phone Realization in Speech Models](https://mlsanity.com/paper/2601.14046/) (2026)
- [Finding My Voice: Generative Reconstruction of Disordered Speech for Automated Clinical Evaluation](https://mlsanity.com/paper/2509.19231/) (2025)
- [Multilingual analysis of intelligibility classification using English, Korean, and Tamil dysarthric speech datasets](https://mlsanity.com/paper/2209.13260/) (2022)

## Built on by (most cited first, 4 of 4)

- [An Empirical Recipe for Universal Phone Recognition](https://mlsanity.com/paper/2603.29042/) (2026)
- [Training-Free Cross-Lingual Dysarthria Severity Assessment via Phonological Subspace Analysis in Self-Supervised Speech Representations](https://mlsanity.com/paper/2604.10123/) (2026)
- [Phonological Subspace Collapse Is Aetiology-Specific and Cross-Lingually Stable: Evidence from 3,374 Speakers](https://mlsanity.com/paper/2604.21706/) (2026)
- [Towards Dys-XAI: Influence-Based Explanations for Dysarthria Severity Assessment](https://mlsanity.com/paper/2606.21306/) (2026)

## Abstract (arXiv)

The growing prevalence of neurological disorders associated with dysarthria motivates the need for automated intelligibility assessment methods that are applicalbe across languages. However, most existing approaches are either limited to a single language or fail to capture language-specific factors shaping intelligibility. We present a multilingual phoneme-production assessment framework that integrates universal phone recognition with language-specific phoneme interpretation using contrastive phonological feature distances for phone-to-phoneme mapping and sequence alignment. The framework yields three metrics: phoneme error rate (PER), phonological feature error rate (PFER), and a newly proposed alignment-free measure, phoneme coverage (PhonCov). Analysis on English, Spanish, Italian, and Tamil show that PER benefits from the combination of mapping and alignment, PFER from alignment alone, and PhonCov from mapping. Further analyses demonstrate that the proposed framework captures clinically meaningful patterns of intelligibility degradation consistent with established observations of dysarthric speech.